X chromosome inactivation (XCI) balances sex chromosome dosage by randomly silencing one X-chromosome in each cell in the early embryo. Which chromosome is silenced is then clonally inherited through all subsequent cell divisions. This leaves each female mammal with a stable mosaic of cells expressing either the maternal or paternal X haplotype. This variability is propagated from early development and persists into adulthood. In this work, we discuss how this variability can be decoded across both individuals and cells to reveal developmental history and regulatory impacts. At the population level, the distribution of XCI skew across individuals reflects the number of cells present at the time of inactivation: more cells at that moment means less variance in the ratio among adults. By fitting models to population-scale XCI distributions across thousands of individuals and ten mammalian species, we show that embryonic cell counts at the time of lineage specification can be estimated from the variance itself, treating noise as the signal rather than the background. At the cellular level, the same mosaic structure allows us to perform within-individual comparisons that are unavailable through conventional approaches. Inferring X haplotype identity in single cells allows direct comparison of cells carrying different haplotypes within the same individual, holding genetic background and environment constant. This reveals structured patterns of regulatory variation that between-individual comparisons cannot resolve. Together, these research themes position XCI as a lens on both developmental history and regulatory variation, with the distribution of a stochastic mark across cells serving as a quantitative record of early decisions that cannot be directly observed.
Dr. Gillis is an expert in integrated neurophysiology at the Donnelly Centre in the University of Toronto. His lab focuses on characterizing the flow of information from cellular gene networks to whole organism phenotypes across species using functional genomics data, focused primarily on the brain.
| 01 | Sunflower (Helianthus annuus) pangenome interrogates disease and drought resistance for crop innovation |
| Esme Padgett (Padgett, E; Todesco, M) | |
| Pangenome, non-human, plant genomics, crop breeding |
| 02 | Single, additive or interactive: Dissecting gene-environment contributions to genome-wide DNA methylation at birth |
| Erick Navarro-Delgado (Navarro-Delgado, EI; Konwar, C; Merrill, SM; MacIsaac, JL; Liang, X; Zhao, Q; Mozhui, K; LeWinn, KZ; Bush, NR; CANDLE study team; Kobor, MS; Korthauer K.) | |
| Epigenetics, gene-environment interaction, exposome, early life, human |
| 03 | SPECIES-SPECIFIC ANTIMICROBIAL ACTIVITY PREDICTION WITH BIOLOGICAL LARGE LANGUAGE MODEL-BASED METHODS |
| Berke Ucar (Ucar, B; Coombe, L; Warren, RL; Birol, I; PeptAid Consortium) | |
| Machine Learning, AMP, LLM |
| 04 | Single-cell transcriptomic analysis predicts endothelial cell subsets that communicate with immune cells via the PD-1 and TIGIT pathways in non-small cell lung cancer |
| Cathy Yan (Yan, C; Wu, FTH; Naso,J; Trinh, D; Bailey, M; Jin, D; Laskin, J; Ho, C; Marra, MA) | |
| non-small cell lung cancer, endothelial cells, single-cell RNA-seq, immunotherapy |
| 05 | Pathology Report Representation Learning for Patient Outcome Prediction |
| Ali Khajegili Mirabadi (Khajegili Mirabadi, A; Fallahpour, G.; Arab, A; Farahani, H.; Bashashati, A.) | |
| AI, Pathology, Cancer Risk Estimation, Large Language Models, Vision Language Models |
| 06 | Interpretable CVAE Reference Mapping Reveals Malignant Hepatocyte Subtypes in HCC Across Studies |
| Selina Sun (Sun, S; Steif, A) | |
| Cancer, AI, single-cell, Liver Cancer, Computational Biology |
| 07 | Interrogating Single-Nucleus RNA-seq Data to Chart Reproducible Regulatory Patterns: Insights from Cell-Type and Condition-Specific Coexpression in the Human Brain |
| Nairuz Elazzabi (Nairuz Elazzabi; Paul Pavlidis) | |
| Cell type specificity, Transcription factor coexpression, Gene regulatory networks, Cross-dataset reproducibility, Alzheimer's disease |
| 08 | Expanding the gene editing toolkit to decipher endogenous causal variants in the genome |
| Asfar Lathif Salaudeen (Salaudeen AL, Shyiak T, Mateyko N, de Boer CG) | |
| Genome engineering, CRISPR, regulatory regions, Mutagenesis, Variant effects |
| 09 | Improving Epigenetic Age Estimation by Combining Epigenetic Clocks |
| Denitsa Vasileva (Vasileva, D; Greenwood, CMT; Daley, D) | |
| epigenetics, DNA methylation, aging |
| 01 | Bowel preparation promotes pathogen colonisation and exacerbates inflammation in humanised IBD models |
| Imogen Porter (Porter, I*; Clayton, C*; Deng, B; Ng, K; Pannu, S; Tropini, C) | |
| microbiota, IBD, bowel preparation |
| 02 | Phylogenetic clustering analysis shows diverse transmission contexts for transgender people living with HIV in British Columbia, Canada |
| Giuli Sucar (Sucar, G; Joy, J; Montaner, J; Toy, J; Sereda, P; Brumme, C) | |
| HIV, Phylogenetics, Transgender |
| 03 | Characterizing species-specific ecological dynamics and genomic adaptations to osmotic perturbations in the gut |
| Hans Ghezzi (Ghezzi, H; Wolff, R; Jain, A; Ng, KM; Burckhardt, J; Garud, N; Tropini, C) | |
| Microbiome, Perturbations, Adaptation, Growth rate, Mortality |
| 04 | AI-Driven Biomarker Identification for Bevacizumab Treatment in High-Grade Serous Ovarian Cancer using Whole Slide Images |
| Mayur Mallya (Mallya, M; Grube, M; Farahani, H; Anglesio, M; Kommoss, S; Bashashati, A) | |
| AI, ovarian cancer, treatment guidance |
| 05 | Peptide Clinical Trial Annotation and Outcome Prediction |
| Emily Zhang (Zhang, E; Birul, U; Birol, I) | |
| Machine Learning, AI, LLMs, Clinical Trials, Peptide Therapeutics |
| 06 | Influence of Genetic Variants on Response to Morphine Alternatives in Pediatric Patients: A Systematic Review |
| Laura Simonson (Simonson, LP; Mufti, K; Scott, EN; Loucks, CM) | |
| pharmacogenomics, pain management, pediatrics, opioids |
| 07 | REAL-TIME PROSTATE CANCER GLAND GLEASON PATTERN SCORING USING AI-ASSISTED RAMAN MICROSCOPY |
| Hasti Jalali (Jalali, H; Sheng, M; Lough, L; Namekawa, T; Belanger, E; Mannas, M; Hach, F) | |
| Prostate Cancer, AI, Gleason Pattern Scoring |
| 08 | A transformer-based foundational model for the vaginal microbiome |
| Dollina Dodani (Dodani, D; Blanco, N; Aboofazeli M; Pradhan T; Talhouk, A) | |
| Vaginal microbiome, Foundational models, Self-supervised learning, Transformers |
| 09 | Quantitative Tissue Topology as a Biomarker of Prostate Cancer Aggressiveness |
| Willie Wu (Wu, W; Inaba, F; Chen, Z; Carraro, A; MacAulay, C; Pukl, M; Keyes, M; Guillaud, M) | |
| Graph-based modeling, Prostate cancer, Tissue architecture |
| 10 | Structural and Inflammatory Changes Following ETI Therapy in Cystic Fibrosis |
| Josh Dyce (Dyce, J; Jang, J; Quon, B) | |
| Cystic Fibrosis, Inflammatory Endotypes, Computed Tomography, Feature Extraction |
| 11 | Enzymatic fragmentation and individualized control pools improve quality of FFPE tumour sequencing |
| Andrew Murtha (Murtha, AJ; Bacon, JVW; Azzam, K; Ng, S; Koudjanian, M; Donnellan, G; Bernales, CQ; Fung, E; Wang, G; Annala, M; Wyatt, AW) | |
| prostate cancer, FFPE, tumour sequencing, optimization |
| 12 | Enhancing Nanopore Assembly Quality at the Basecalling and Polishing Stages |
| Parham Kazemi (Kazemi, P; Birol, I) | |
| nanopore sequencing, genome assembly, basecalling |
| 13 | Multimodal Prediction of Clinical Outcomes in Patients with Hypertrophic Cardiomyopathy |
| Raam Sivakumar (Sivakumar, R; Laksman, Z; Singh, Amrit) | |
| Deep Learning, Machine Learning, Medical Imaging |
| 14 | A biophysically inspired model of transcriptional regulation in hormone signalling |
| Hoda Taeb (Taeb, H; Safaeesirat, A; Tekoglu, TE; Lack, N; Emberly, E) | |
| modeling, transcriptional regulation, cancer, enhancer activity |
| 01 | Identification of DNA sequence motifs enriched in regulatory regions of genes escaping X-chromosome inactivation |
| Aditi Srinivasan (Srinivasan, A) | |
| X-Chromosome, Sequential Analysis, Computational Biology, Machine Learning |
| 02 | Inference-time enhanced sampling of diffusion models with Metadynamics |
| Alireza Omidi (Omidi, A; Syed, S; Gsponer, J) | |
| diffusion models, Metadynamics, enhanced sampling |
| 03 | Mapping metabolic networks in a full-scale anaerobic digester using stable isotope probing metagenomics |
| Alma Garcia Roche (Garcia Roche, AR; Waring, K; Madill, M; Friedline, SE; Ziels, RM) | |
| microbial ecology, anaerobic digestion, metagenomics, viromics, stable isotope probing |
| 04 | Socioeconomic Determinants and Biological Aging: Exploring the Potential Mediating Role of Environmental Exposures in the Canadian Longitudinal Study on Aging |
| Amanda Kurowski (Kurowski, A; Engelbrecht, HR; Kobor, MS; Stringhini, S) | |
| Socioeconomic conditions, environmental conditions, DNA methylation, epigenetic aging, mediation analysis |
| 05 | A matrix-centered view of mass spectrometry platform innovation for volatilome research |
| Andras Szeitz (Szeitz, A; Sutton, AG; Hallam, SJ) | |
| VOC, SIFT-MS, PTR-MS, Orbitrap-MS, GCxGC-TOF-MS |
| 06 | Defining the landscape of potential AID binding sites after knockdown of KMT2D or ARID1A in Mino cells |
| Andrew Chen (Chen, AY; Uday, P; Hilton, L; Weng, A) | |
| Lymphoma, chromatin accessibility, AID targeting, breakpoints |
| 07 | Benchmarking host DNA depletion and whole-genome amplification strategies for profiling ultra-low biomass microbiomes inhabiting the human respiratory tract |
| Anika Nag (Nag A.; Chen S.Q.; McLaughlin R.J.; Noonan A.J.C.; Capron R.; Bartolomeu C.; Borden S.A.; Myers R.; Lam S.; Hallam S.J) | |
| Lung Cancer, Microbiome, Metagenomics, WGS, Amplicon Sequencing, Microbial Biomarkers |
| 08 | The Neighbourhood Matters: Spatial Single-Cell Profiling of Follicular Lymphoma |
| Anne-Sophie Fratzscher (Fratzscher, AS; Lee, E; Wu, S; Scott, DW; Steidl, C; Roth, A) | |
| cancer, follicular lymphoma, single cell spatial transcriptomics, tumour microenvironment |
| 09 | Clonal hematopoiesis after 177Lu-PSMA-617 radioligand therapy in prostate cancer |
| Asli Munzur (Munzur, AD; Herberts, C; Kwan, EM; Emmett, L; Sandhu, S; Buteau, JP; Iravani, A; Joshua, AM; Francis, RJ; Lee, ST; Scott, AM; Martin, AJ; Stockler, MR; Zhang, AY; Williams, SG; Bernales, CQ; Donnellan, G; Koudjanian, M; Parekh, K; Bacon, JVW; Karsan, A; Azad, AA; Davis, ID; Hofman, MS; Wyatt, AW) | |
| prostate cancer, clonal hematopoiesis, clinical trial, translational research, genomics |
| 10 | Cell-type specific genetic-to-epigenetic relationships in the human breast |
| Axel Hauduc (Hauduc, A; Steif, J; Bilenky, M; Moksa, M; Cao, Q; Eaves, C; Hirst, M) | |
| Genetics epigenetic breast variation QTL |
| 11 | GeneExpert: A Foundation Model for Gene Expression Understanding |
| Behnam Maneshgar (Maneshgar, B; Zhang, T; Farahani, H; Bashashati, A) | |
| AI, Foundation Model, Gene Expression |
| 12 | Elevated endogenous retroviral expression in severe COVID-19 patients correlates with innate immune activation markers |
| Bessie Wang (Wang, B; Deckers, T; Liu, E; Tokuyama, M) | |
| Endogenous Retrovirus, COVID-19, RNAseq, scRNAseq |
| 13 | Evaluating AI for Summarizing Variant Interpretation in Precision Oncology: A Benchmark Dataset of Comprehensive Case Reports |
| Caralyn Reisle (Reisle, Caralyn; McConechy, Melissa; Csizmok, Veronika; Wee, Kathleen; Taylor, Greg; Dupuis, John; Grisdale, Cameron J.; Xu, Morgana; Hanos, Melika; Shen, Yaoqing; Chiu, Readman; Tran, Linh; Laskin, Janessa; Marra, Marco A.; Jones, Steven J.M.) | |
| AI, NLP, Cancer, Precision Medicine |
| 14 | Decoding Nosema ceranae and Black Queen Cell Virus (BQCV) Co-Infection in Honeybees through Spatial Multi-Omics |
| Cedar Zhang (Zhang, Y; Alcazar, A; Rogalski, J; Foster, L) | |
| Honeybee, Nosema, Black Queen Cell Virus, Spatial multi-omics, Gut–brain axis |
| 15 | Spatial Transcriptomics Reveals Airway Remodeling and Molecular Targets Across COPD Severity |
| Chen Xi Yang (Yang, C; Rojas-Quintero, J; Gerayeli, FV; Polverino, F; Ng, RT; Malo, J; Sin, DD) | |
| Chronic obstructive pulmonary disease, small airway, spatial transcriptomics |
| 16 | Evaluating Clinical Diagnostic Reasoning Under Real-World Uncertainty |
| Cindy Zhang (Cindy Xiao Yu Zhang, Wyeth W. Wasserman, Jian Zhu) | |
| Clinical decision support, Diagnostic reasoning, Clinical plausibility |
| 17 | Domain-Invariant Feature Learning for Generalizable Gene Expression Prediction from Histology Images |
| Elahe Ranjbari (Ranjbari, E) | |
| AI, Domain Generalization, Gene Expression Prediction, Spatial Transcriptomics |
| 18 | MiClone: A Probabilistic Method for Inferring Cell Phylogenies from Mitochondrial Variants |
| Emilia Hurtado (Hurtado, E; Roth, A) | |
| Phylogenetics, Cancer, Mitochondria |
| 19 | Advancing Precision Psychiatry in Schizophrenia through the Identification of Individualized Brain Network Dysfunctions |
| Erica Zeng (Zeng, E; Eickhoff, S; Shahki, J; Woodward, T) | |
| Schizophrenia, Functional Magnetic Resonance Imaging, Task-based Brain Networks, Constrained Principal Component Analysis for fMRI; Biomarkers |
| 20 | Exploring the Impact of H2A.Z Depletion on Nascent Transcription Regulation |
| Eully Ao (Ao, E; Brewis, HT; Kobor, MS) | |
| yeast, H2A.Z, nascent transcription, depletion system |
| 21 | Integrated multi-omics approach for the characterization of no specific molecular profile in endometrial carcinoma |
| Farbod Moghaddam (Moghaddam, F; Cochrane, D; McAlpine, J; Hoang, L; Roth, A; Talhouk, A) | |
| Endometrial Carcinoma, Multi-omics Integration, Molecular Subtyping, Similarity Network Fusion, Machine Learning |
| 22 | Investigating the Genomic Contributions to Familial Intracranial Aneurysms in a First Nation from Northern British Columbia |
| Gage Fairlie (Fairlie, GMJ; Anderson, S; Lehman, A; Arbour, L) | |
| Medical Genetics, Linkage Analysis, Intracranial Aneurysms, WGS, SNP Array |
| 23 | An embryonic stem cell simulator that incorporates biological time |
| Harry Cheng (Cheng, HCM; Abou Chakra, M; Bader, G; Shakiba, N) | |
| Cell cycle, simulator, ESC |
| 24 | Human gene regulatory network inference through a custom Peter-Clark algorithm |
| Herbert Yao (Yao, Herbert; Zhang, Jian; Kiyota, Brett; Yachie, Nozomu) | |
| systems biology, causal discovery, high performance computing, gene regulatory network |
| 25 | A Reproducible Framework to Benchmark Single‑Cell Bisulfite Sequencing with Haplotype‑Resolved Simulations |
| Ivana Sanchez Olivares (Sanchez Olivares, I; Birol, I) | |
| Single-cell methylation profiles, haplotype-resolved reads simulation, reproducible benchmarking framework |
| 26 | Scaling up massive parallel reporter assays with bulk quantitative density-based cell sorting |
| JJ Hum (Hum, JJ; de Boer, CG) | |
| genomics, synthetic biology, cell sorting |
| 27 | Chemogenomic profiling of diverse Saccharomyces cerevisiae strains using BarMix: a novel CRISPR-Cas9 marker-less barcoded library |
| Jackson Moore (Moore, J; Barazandeh, M; Nislow, C; Measday, V) | |
| Saccharomyces cerevisiae, yeast, natural variation, genetic barcoding, chemogenomics |
| 28 | Characterization of a GzmB-Driven Fibrotic Signature in Primary Human Dermal Fibroblasts via Consensus Differential Expression and Drug Repurposing Analysis |
| Jeffrey Tang (Jeffrey S. Tang; Alexandre Aubert; Anna Prudova; Karen Jung; Amrit Singh*; David J. Granville*) | |
| transcriptomics, serine protease, perturbation |
| 29 | MSClust: de novo single-cell bi-sulfite clustering |
| Johnathan Wong (Wong, J; Coombe, L; Warren, RL; Birol I) | |
| Methylation, single-cell, bisfulite, de novo, clustering |
| 30 | Expanding the bacteroides synthetic biology toolkit to develop an in vivo intestinal malabsorption biosensor. |
| Juan Camilo Burckhardt Acevedo (Burckhardt, Juan C; McCallum, Giselle; He, Jerry; Hong, Alice; Tropini, Carolina) | |
| Bacterial Biosensors, Synthetic Biology, Transcriptional Circuits, Microbiome Research |
| 31 | A gene centric analysis of denitrification in the oxygen limited Northeastern Subarctic Pacific |
| Julia Anstett (Anstett, J; Mclaughlin, R; Morgan-Lang, C; Plominsky, AM; Kiesser, A; Chang, T; Pachiadaki, MG; Gavelis, GS; Macartney, K; La Clair, JJ; Weinheimer, A; Brown, JM; Burkart, MD; Ulloa, O; Baltar, F; Juergens, K; Nunoura, T; Sintes, E; Herndl, G; Stepanauskas, R; and Hallam, SJ) | |
| Oxygen Minimum Zones, Metagenomics, Single-Cell Genomics, Gene-centric Phylogenetics |
| 32 | Expanding Strategies for Bacterial Nanocellulose Production from Organic Wastes |
| Julia Desbiens (Desbiens, JC; Lewicki, E; Joshi, J.) | |
| non-human, synthetic biology, biomaterials, sustainability |
| 33 | Improving Monitoring of Environmental Effects of Fish Net Pens by Meiofauna Metabarcoding |
| Julia Price (Price, J; Hauser, L; Nel, R; Dias, J; Dickey, J; Schmidt, D) | |
| non-human, conservation, metabarcoding |
| 34 | Exploring therapeutic opportunities in p53abn Endometrial Carcinomas |
| Juliana Sobral de Barros (Sobral de Barros, J; Cochrane, D; Jamieson, A; Senz, J; McAlpine, JN; Huntsman, DG) | |
| endometrial cancer, p53 abnormal, Cyclin E1, targeted therapy |
| 35 | High-Throughput Characterization of the Filamentous Cyanobacterium Sodalinema yuhuli AB48[ |
| Kalen Dofher (Dofher, K; Sukkasam, N; Liu, T; Hallam, SJ) | |
| Cyanobacteria, Bioproducts, Wastewater, High-Throughput, Characterization |
| 36 | Mast cells as biomarkers for capecitabine benefit in triple negative breast cancer |
| Katherine Rich (Rich, K; Shenasa, E; Gao, D; Bashashati, A; Nielsen, T) | |
| breast cancer, AI, biomarkers |
| 37 | Investigating the Complexity of Genomic Epidemiology and Evolution of Tenacibaculum spp. in Wild and Aquaculture Salmon Populations in British Columbia |
| Kaytlyn Tasalloti (Tasalloti K, Deeg C, Mordecai G, Joy J) | |
| infectious disease, fisheries, conservation, genomics |
| 38 | Evaluating the evolutionary and developmental impact of mobile genetic elements in vertebrates |
| Keiran Maskell (Keiran Maskell, Nanami Masuyama, and Nozomu Yachie) | |
| mobile genome, transposable elements, vertebrate biology, evolutionary developmental biology |
| 39 | Hydrogel bead display for large sequence-t0-function datasets in protein engineering |
| Kenyon Alexander (Alexander, K; Mateyko, N; deBoer, C) | |
| ML, protein engineering, microfluidics, emulsion PCR, cell-free protein expression |
| 40 | Statistical variations on metabolomics data quality |
| Kevin Zhang (Yikang, Z; Brian, L; Sangpei J; Tao H) | |
| Statistics, Metabolomics, data quality |
| 41 | Multi-Modal Meta-Analysis of Functional Genomics Data to Identify Regulatory Relationships in the Brain |
| Kevin Zhang (Zhang, K; Pavlidis, P) | |
| meta-analysis, regulation, brain |
| 42 | Characterization of CXCR5-CXCL13 axis in classic Hodgkin lymphoma |
| Makoto Kishida (Kishida, M; Rai, S; Yin, Y; Aoki, T; Steidl, C) | |
| Lymphoma, Tumor microenvironment, humanized mice model |
| 43 | Decoding the Multiomic Signatures of Oral Cancer Progression |
| Maple Lei (Maple Lei, Kelly Yi Ping Liu, Catherine F. Poh, Steven Jones) | |
| Cancer, machine-learning, biomarker, transcription, methylation |
| 44 | Evaluating ctDNA as a tool for tumour genotyping and patient prognostication in metastatic urothelial cancer |
| Maria Stephenson (Stephenson, M; Pham, J; Rostin, K; Ng, SWS; Murtha, A; Bernales, CQ; Donnellan, G; Parekh, K; Bacon, J; Annala, M; Müller, DC; Eigl, BJ; Ozgun G; Black, P; Maurice-Dror, C; Chi, KN; Vandekerkhove, G; Wyatt, AW) | |
| cancer, circulating tumour DNA, biomarkers, prognosis |
| 45 | Hunting for mediation in expression quantitative trait loci: a case-study using ovarian cancer |
| Maxwell Douglas (Douglas, JM; Park, YJ) | |
| Statistical Genetics, Ovarian Cancer, Causal Inference, Mediation |
| 46 | Genetic Variation in TRMT9B, RORA, and ALDH1A2 Predicts the Development of Painful Chemotherapy-Induced Toxicities in Children with Cancer |
| Mia Simmons (Simmons, ME; Scott, EN; Ernest-Hoar, G; Carleton, BC; Rassekh, SR; Ross, CJD; Loucks CM) | |
| Pharmacogenomics, Pain management, Caenorhabditis elegans |
| 47 | Single-Cell RNA Sequencing (scRNA-seq) Of Asthmatic Individuals Exposed To Traffic-Related Air Pollution And An Inhaled Corticosteroid |
| Michael Yoon (Yoon, M; Ryu, MH; Zhao, A; Lau, K; Yuen, A; Rider, CF; Singh, A; Carlsten, C) | |
| air pollution, scRNA-seq, diesel exhaust, exposure, omics |
| 48 | Semantically informed embedding of differential expression contrasts |
| Moritz Aubermann (Aubermann M; Pavlidis, P) | |
| Deep Learning, Differential expression, Transcriptomics |
| 49 | Multimodal Single-Cell Analysis Reveals Immune-Driven Tumor Remodeling in 4T1 TNBC models |
| Naila Adam (Adam, N; Sepulveda, L; O’Flanagan, C; Paez-Ribes, M; Gonzalez-Solares, E; Mulvey, C; Vázquez-García, I; Roth, A; Shah, SP; Aparicio, S; Bressen, D; Hannon, GJ) | |
| 50 | Enrichment Analysis of Differential Expression Patterns in a Large Corpus |
| Neera Patadia (Patadia, N; Pavlidis, P) | |
| transcriptomics, meta-analysis, data harmonization, condition enrichment |
| 51 | Combinatorial barcoded bead synthesis for scaling pooled gene assembly |
| Nick Mateyko (Mateyko, N; Alexander, K; Plesa, C; de Boer, C) | |
| Gene synthesis, DNA assembly, synthetic biology, barcoded beads, enzyme screening |
| 52 | SPATIAL PROFILING OF THE TUMOR IMMUNE MICROENVIRONMENT IN MUSCLE-INVASIVE BLADDER CANCER TREATED WITH NEOADJUVANT PLATINUM CHEMOTHERAPY |
| Nikolay Alabi (Nikolay Alabi, Nicolas Zheng, Joshua Scurll, Nemat Haroon, Jussi Nikola, Htoo Z Oo, Katy Milne, Brad Nelson, Ali Bashashati, Morgan Roberts, Alberto Contreras-Sanz, Shilpa Gupta, Peter Black) | |
| computational biology, spatial modelling, bladder cancer, biomarker discovery |
| 53 | Exploring Neurodevelopmental Impacts of SETD2 Mutations Through Bulk and Single-Cell Multi-omics |
| Parsa Seyfourian (Seyfourian, P; Yeh, E; Blume, L; Azarafshar, P; Park, Y; Chen, C) | |
| Neurodevelopment, Epigenomics, Multi-omics, Neuroinformatics, and Cerebral Organoids |
| 54 | Automated cell-type annotation for meta-analysis of neuropsychiatric disease |
| Rachel Schwartz (Schwartz, Rachel; Pavlidis, Paul) | |
| single cell, neuroscience, machine learning, meta-analysis |
| 55 | Retrospective cell clone isolation using protein barcodes |
| Ren Takimoto (Takimoto, Ren; Pérez Hidalgo, Diego; Mori, Hideto; Yachie, Nozomu) | |
| Retrospective clone isolation, prptide barcoding, heterogeneity |
| 56 | How vaccines shape B cell evolution |
| Rituparna Banerjee (Banerjee, R; Pennell, M; Coombs, D) | |
| B cells, vaccinations, phylogenetic trees, mathematical modelling |
| 57 | Data-Efficient Self-Supervised DNA Language Modeling for Nematode Genomes |
| Robin Li (Li, R) | |
| Large language model; DNA language model; unsupervised discovery |
| 58 | Plasma cell-free DNA mapping of TP53, PTEN, and RB1 allelic disruption and association with adverse outcomes in metastatic prostate cancer |
| Ruby Liao (Liao, YJR; Tolmeijer, SH; Wang, CK; Xie, TTY; Roberts, HN; Herberts, C; Ng, SWS; Parekh, K; Kwan, EM; Sandhu, S; Mehra, N; Bergman, AM; Hofman, M; Seymour, L; Annala, M; Chi, KN; Maurice-Dror, C; Wyatt, AW) | |
| prostate cancer, genomics, liquid biopsy |
| 59 | Recovery of a novel lineage of sulfur oxidizing denitrifiers in the Saanich Inlet water column using single-cell scaffold-anchored binning |
| Ryan McLaughlin (McLaughlin, RJ; Kieft, B; Morgan-Lang, C; Anstett, J; Hallam, SJ;) | |
| oxygen minimum zone, denitrification, sulfur oxidation, metagenome-assembled genome, single-cell amplified genome |
| 60 | Building a Computational Pipeline for Cardiovascular Drug Repurposing |
| Samuel Leung (Leung, S; Wang, Y; Singh, A;) | |
| Cardiovascular Disease, Drug Repurposing, Pipeline Development, Systematic Benchmarking |
| 61 | Pan-genomic Analysis Reveals Genomic Plasticity and Adaptation Mechanisms in Puccinia triticina |
| Sean Formby (Formby, S; Kim, SH ; Holmes, J ; Lining, R ; Holden, S ; Brar , GS ; Hallam, SJ ; Fellers, J ; Bakkeren , G) | |
| pangenomics, genome assembly, GWAS, population genomics, agriculture |
| 62 | Computational Analysis and Prediction of Tissue Specific Phosphorylation of Intrinsically Disordered Protein Regions |
| Sofie Hooft Toomey (Hooft Toomey, Sofie; Gsponer, Joerg) | |
| Phosphorylation, Intrinsically Disordered Regions, Proteins, Protein-protein Interactions |
| 63 | Validation of Raman Process Analytics in T-cell Manufacturing Through Biochemical Quantitation of its Macromolecular Components |
| Syd Wong (Wong, S.E.Z.; Sherwood, C.S.; Piret, J.M.) | |
| Biochemical assay, Raman spectroscopy, process analytics, macromolecular quantification |
| 64 | Comprehensive Population Genetic Clustering of Diverse Human Genomes for Ancestry-Informed Reference Panel Development |
| Taghrid Aloraini (Aloraini, T; Rajan-Babu, IS; Warren, RL; Coombe, L; Friedman, JM; Birol, I) | |
| Ancestry, population, reference panel |
| 65 | Associations Between Anthracosis and Molecular Dysregulation of Human Lung Tissue |
| Taysia Nikaido-Landry (Nikaido-Landry, T; Fung, L; Lo, T; Lim, E) | |
| Lung, anthracosis, exposures, spatial transcriptomics |
| 66 | Spatially resolved immune microenvironment of recurrent triple-negative breast cancer |
| Tina Hsu (Hsu, T; Lee, E; Richter, A, Kong E; Llanos, V; Flores, C; Park, Y; Aparicio, S) | |
| Spatial biology, Triple-negative breast cancer, Tumour heterogeneity |
| 67 | Multimodal Integration of Spatial Transcriptomics and Foundation Model–Derived Imaging Features for Acute Cardiac Allograft Rejection |
| Tony Liang (Liang, C. T. ; Singh, A) | |
| Heart transplantation, AI, Multimodal integration, Spatial Transcriptomics, Computational pathology |
| 68 | Enter the Cyanoverse, a database of cyanobacteria and their co-occurring microorganisms at different levels of biological organization |
| Tony Liu (Liu, XT; Hyland, S; Collins, J; Hallam, SJ) | |
| Ecology, Cyanobacteria, Metagenomics, Database, Nextflow |
| 69 | Transcriptomic response to stressful temperatures in a resynthesized polyploid, Brassica napus, and its progenitors, B. oleracea and B. rapa |
| Tonya Severson (Tonya F. Severson, Jeannette Whitton, Jörg Bohlmann, and Keith L. Adams) | |
| polyploidy, abiotic stress, alternative splicing, expression analysis |
| 70 | The Dynamic Changes in The Classical Hodgkin Lymphoma Tumor Microenvironment Using Single Cell Analysis |
| Yifan Yin (Yin,Y;Aoki T; Steidl C) | |
| Cancer, single-cell, tumor microenvironment |
| 71 | Unsupervised Discovery of Spatial Niches via Contrastive Graph Representation Learning in Multi-Sample Spatial Transcriptomics |
| Yiyang Wang (wang, yiyang) | |
| AI, spatial transcriptomics, GNN |